Based on image super resolution and boosted feature pyramid dehazing network

DOI:	10.11809/bqzbgcxb2023.11.038
Keywords:	image dehazing; boosted algorithm; super resolution; feature pyramid
Abstract:	Using a single convolutional neural network dehazing algorithm is prone to low contrast, loss of detail information and incomplete dehazing. In order to solve the above problems, a boosted pyramid model and image super resolution parallel demisting network structure are proposed. The boost algorithm acts on the feature pyramid image reconstruction process to improve the signal to noise ratio of the defogging image. Channel attention maps the feature information extracted by the encoder to the decoder, giving each channel different weights, so as to improve the efficiency of dehazing. The super resolution network adds more high frequency feature details to improve the clarity of the dehazing image. Experiments show that the boosted pyramid and super resolution network have strong dehazing ability, and their performance is better than other methods, which can effectively suppress the degradation of the output image resolution of a single convolutional neural network.
Issue:	Vol. 44 No. 11 (2023)
Published:	2023-11-28
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